The present disclosure is generally related to the field of printing, and other processes based on the continuous deposition of material, including industrial molding or extrusion processes, and more particularly to techniques for grouping object quality anomalies in a specified region of an object based on context-specific characterization and shared descriptive profile features.
In many industrial processes, for example in extrusion or molding processes, the product may contain defects that appear as streaking in the direction of the manufacturing process. For example, the defects might take the form of ridges in the finished material. In the printing industry, the same type or a similar defect may result in dark or light lines across or through the printed image. Different process faults can cause different patterns of streaking in the finished materials. Defects are generally characterized within the printing industry as “streaking” if the defect occurs along the process direction of the printed or manufactured product, or as “banding” or another periodic defect if it appears in the cross-process direction.
Previous work with regard to streaking has focused mainly on detecting the streak or process defect, and on characterizing the streaks or defects, including techniques related to model-based diagnosis. Diagnosis of streaking in industrial processes is largely a manual process that requires trained technicians with direct experience on the equipment in question.
In the printing industry, diagnosis has focused predominantly on streak detection and analysis, and on a clustering-related technique that provides a generic mechanism for the clustering of streaks based solely on visual features. Though these techniques are available for use with regard to identifying streaks, none focus specifically on interpreting the identified streaks. More specifically, no technique provides for focusing on the characterization and classification of streaks using a specific formulation of features for recognizing a cluster of similar streaks, and then for further identifying those streaks in the cluster that share more focused similarities based on a given set of descriptors.